analytic engine

The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty.
The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors:
Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression.
Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It

Enterprises use data virtualization software such as TIBCO® Data Virtualization to reduce data bottlenecks so more insights can be delivered for better business outcomes. For developers, data virtualization allows applications to access and use data without needing to know its technical details, such as how it is formatted or where it is physically located. For developers, data virtualization helps rapidly create reusable data services that access and transform data and deliver data analytics with even heavylifting reads completed quickly, securely, and with high performance. These data services can then be coalesced into a common data layer that can support a wide range of analytic and applications use cases. Data engineers and analytics development teams are big data virtualization users, with Gartner predicting over 50% of these teams adopting the technology by 202

In the new age of big data, applications are leveraging large farms of powerful servers and extremely fast networks to access petabytes of data served for everything from data analytics to scientific discovery to movie rendering. These new applications demand fast and efficient storage, which legacy solutions are no longer capable of providing.

Expanding analytic capabilities are critical to digitizing the business, optimizing costs, accelerating innovation, and surviving digital disruption
Historically, manufacturers were almost solely focused on reducing costs by applying automation and analytics to engineering, R&D, manufacturing operations, and quality organizations. Even though the strategies used within these areas are still needed, they are not sufficient to ensure business survival and continuity in the age of Industry 4.0 and the IoT.
Today, it is paramount that smart manufacturers broaden their scope because disruptive innovations in data acquisition, storage, and analytics technology have enabled an entirely new degree of automation and virtualization, promising a complete 360-degree high-fidelity virtual data-driven integrated views of all operations—from suppliers and supply chains, through equipment, processes, and manufacturing practices, to final product testing and customer satisfaction.
Download this paper

TIBCO Spotfire® Data Science is an enterprise big data analytics platform that can help your organization become a digital leader. The collaborative user-interface allows data scientists, data engineers, and business users to work together on data science projects. These cross-functional teams can build machine learning workflows in an intuitive web interface with a minimum of code, while still leveraging the power of big data platforms.
Spotfire Data Science provides a complete array of tools (from visual workflows to Python notebooks) for the data scientist to work with data of any magnitude, and it connects natively to most sources of data, including Apache™ Hadoop®, Spark®, Hive®, and relational databases. While providing security and governance, the advanced analytic platform allows the analytics team to share and deploy predictive analytics and machine learning insights with the rest of the organization, white providing security and governance, driving action for the business.

In today’s world, it’s critical to have infrastructure that supports
both massive data ingest and rapid analytics evolution. At Pure
Storage, we built the ultimate data hub for AI, engineered to
accelerate every stage of the data pipeline.
Download this infographic for more information.

Gathering machine sensor data for analysis across a power plant is only the beginning. The Digital Twin takes analytics to the next level by creating a virtual analytic model of assets within the plant. With the GE Digital Twin power companies can understand how assets are performing in real time, and predict future performance.

In the domain of data science, solving problems and answering questions through data analysis is standard practice. Data scientists experiment continuously by constructing models to predict outcomes or discover underlying patterns, with the goal of gaining new insights. But data scientists can only go so far without support.

IBM Planning Analytics Local is the on-premises version of the planning solution built on the powerful, in-memory OLAP engine of IBM TM1. It automates your planning, budgeting and forecasting, and helps you link operational tactics with financial plans.

Download this whitepaper to learn how Hortonworks Data Platform (HDP), built on Apache Hadoop, offers the ability to capture all structured and emerging types of data, keep it longer, and apply traditional and new analytic engines to drive business value, all in an economically feasible fashion. In particular, organizations are breathing new life into enterprise data warehouse (EDW)-centric data architectures by integrating HDP to take advantage of its capabilities and economics.

The Software in Silicon design of the SPARC M7 processor, and the recently announced SPARC S7 processor, implement memory access validation directly into the processor so that you can protect application data that resides in memory. It also includes on-chip Data Analytics Accelerator (DAX) engines that are specifically designed to accelerate analytic functions. The DAX engines make in-memory databases and applications run much faster, plus they significantly increase usable memory capacity by allowing compressed databases to be stored in memory without a performance penalty.
The following Software in Silicon technologies are implemented in the SPARC S7 and M7 processors:
Note: Security in Silicon encompasses both Silicon Secured Memory and cryptographic instruction acceleration, whereas SQL in Silicon includes In-Memory Query Acceleration and In-Line Decompression.
Silicon Secured Memory is the first-ever end-to-end implementation of memory-access validation done in hardware. It

What happened to Moore’s Law? Why is Oracle releasing a new SPARC processor with fewer cores and threads? The SPARC S7’s core processors are 50 percent to 100 percent more efficient than x86 processors. SPARC S7 delivers up to 10 times more-efficient data analytics and machine learning than x86-based systems. The new SPARC S7 is available in servers, an engineered system, and a cloud service at the same price as the Oracle Cloud x86 Compute service. The SPARC S7 processor makes it possible to build powerful, scalable, and cost-effective systems be it for application scale-out, on premise or public cloud.

In January 2015, Acrolinx launched a research project to read and evaluate the world’s content. Using a proprietary linguistic analytics engine, our software reviewed 150,000 individual, public-facing web pages from 340 companies around the world. That represents 20 million sentences and over 160 million words.
Read this white paper to read the findings of this research report and the effect of content.

Discover a new way of looking at SEO by focusing on prioritization, content development and analytics to lead to Search Engine Success. Learn how managing your interactive portfolio and measuring analytics can increase your ROI.

Download this solutions guide to get a technical overview on building Hadoop on NetApp E-series storage and learn how it will effectively help deliver big analytics with pre-engineered, compatible, and supported solutions ultimately reducing the cost, schedule, and risk of do-it-yourself systems.

This guide provides the framework to build a successful analytics foundation in your organization, and shows you how to create an effective analytics measurement program that provides actionable insights and results driven recommendations.

As the pace of business continues to accelerate, forward-looking organizations are beginning to
realize that it is not enough to analyze their data; they must also take action on it. To do this, more
businesses are beginning to systematically operationalize their analytics as part of a business process.
Operationalizing and embedding analytics is about integrating actionable insights into systems and
business processes used to make decisions. These systems might be automated or provide manual,
actionable insights. Analytics are currently being embedded into dashboards, applications, devices,
systems, and databases. Examples run from simple to complex and organizations are at different
stages of operational deployment. Newer examples of operational analytics include support for
logistics, customer call centers, fraud detection, and recommendation engines to name just a few.
Embedding analytics is certainly not new but has been gaining more attention recently as data
volumes and the freq

Teams of engineers and statisticians spend their days immersed in seas of data from various sources. However, it isn't just the largest of organizations that can benefit from analytics and data-driven decision-making. Businesses of all sizes need to leverage the new currency of data and information.